Investigative Magnetic Resonance Imaging

Korean Society of Magnetic Resonance in Medicine (대한자기공명의과학회)
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DEMO: Deep MR Parametric Mapping with Unsupervised Multi-Tasking Framework

  • Cheng, Jing (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences) ;
  • Liu, Yuanyuan (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences) ;
  • Zhu, Yanjie (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences) ;
  • Liang, Dong (Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences)
  • Received : 2021.06.07
  • Accepted : 2021.09.17
  • Published : 2021.12.30
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Abstract

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

Keywords

Acknowledgement

This work was supported partly by the National Natural Science Foundation of China (61771463, 81830056, U1805261, 81971611, 61871373, 81729003, 81901736), National Key R&D Program of China (2017YFC0108802 and 2017YFC0112903), Natural Science Foundation of Guangdong Province (2018A0303130132), Shenzhen Peacock Plan Team Program (KQTD20180413181834876), Innovation and Technology Commission of the government of Hong Kong SAR (MRP/001/18X), Strategic Priority Research Program of Chinese Academy of Sciences (XDB25000000), China Postdoctoral Science Foundation (2021M693316), and SIAT Innovation Program for Excellent Young Researchers (E1G031).

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